Operations research in disaster preparedness and response: The public health perspective

Operations research is the scientific study of operations for the purpose of better decision making and management. Disasters are defined as events whose consequences exceed the capability of civil protection and public health systems to provide necessary responses in a timely manner. Public health science is applied to the design of operations of public health services and therefore operations research principles and techniques can be applied in public health. Disaster response quantitative methods such as operations research addressing public health are important tools for planning effective responses to disasters. Models address a variety of decision makers (e.g. first responders, public health officials), geographic settings, strategies modelled (e.g. dispensing, supply chain network design, prevention or mitigation of disaster effects, treatment) and outcomes evaluated (costs, morbidity, mortality, logistical outcomes) and use a range of modelling methodologies. Regarding natural disasters the modelling approaches have been rather limited. Response logistics related to public health impact of disasters have been modelled more intensively since decisions about procurement, transport, stockpiling, and maintenance of needed supplies but also mass vaccination, prophylaxis, and treatment are essential in the emergency management. Major issues at all levels of disaster response decision making, including long-range strategic planning, tactical response planning, and real-time operational support are still unresolved and operations research can provide useful techniques for decision management.-JRC.G.2-Global security and crisis managemen

A Multi-Criteria Vertical Coordination Framework for a Reliable Aid Distribution

Purpose: This study proposes a methodology that translates multiple humanitarian supply chain stakeholders’ preferences from qualitative to quantitative values, enabling these preferences to be integrated into optimization models to ensure their balanced and simultaneous implementation during the decision-making process. Design/methodology/approach: An extensive literature review is used to justify the importance of developing a strategy that minimizes the impact of a lack of coordination on humanitarian logistics decisions. A methodology for a multi-criteria framework is presented that allows humanitarian stakeholders’ interests to be integrated into the humanitarian decisionmaking process. Findings: The findings suggest that integrating stakeholders’ interests into the humanitarian decision-making process will improve its reliability. Research limitations/implications: To further validate the weights of each stakeholder’s interests obtained from the literature review requires interviews with the corresponding organizations. However, the literature review supports the statements in this paper. Practical implications: The cost of a lack of coordination between stakeholders in humanitarian logistics has been increasing during the last decade. These coordination costs can be minimized if humanitarian logistics’ decision-makers measure and simultaneously consider multiple stakeholders’ preferences. Social implications: When stakeholders’ goals are aligned, the humanitarian logistics response becomes more efficient, increasing the quality of delivered aid and providing timely assistance to the affected population in order to minimize their suffering. Originality/value: This study provides a methodology that translates humanitarian supply chain stakeholders’ interests into quantitative values, enabling them to be integrated into mathematical models to ensure relief distribution based on the stakeholders’ preferences.Peer Reviewe

A lexicographical dynamic flow model for relief operations

Emergency management is a highly relevant area of interest in operations research. Currently the area is undergoing widespread development. Furthermore, recent disasters have highlighted the importance of disaster management, in order to alleviate the suffering of vulnerable people and save lives. In this context, the problem of designing plans for the distribution of humanitarian aid according to the preferences of the decision maker is crucial. In this paper, a lexicographical dynamic flow model to solve this problem is presented, extending a previously introduced static flow model. The new model is validated in a realistic case study and a computational study is performed to compare both models, showing how they can be coordinated to improve their overall performance

Disaster Response Project Scheduling Problem: A Resolution Method based on a Game-Theoretical Model

We present a particular disaster response project scheduling problem (DRPSP) motivated by Fukushima’s nuclear accident of Japan in 2011. We describe the problem as MPS;R,N|prec, dn|Pckf(rk(S)) by using Hartmann and Briskornscheme and formulate a mixed integer linear programming (MILP) model. Due to the NP-hardness of the problem, we propose a resolution method based on game theory.This method associates the DRPSP to a non-cooperative game model, such thatgame solution is a feasible solution of the problem. In order to explore the potentialof the proposed model and the performance of the resolution method, computationalexperiments are carried out. The results of resolution method show on average, thatthe method finds a feasible solution with a difference of 15.44% with respect to optimalsolution within one percent of the time required by the MILP over GAMS22.7.2/CPLEX 11.0

Humanitarian Logistics: A New Field of Research and Action

The article of record as published may be located at http://dx.doi.org/10.1561/0200000014Recent natural disasters such as the earthquake in Hati, Hurricane Katrina in the United States, tsunami in the Indian Ocean, the earthquake in Pakistan, and numerous humanitarian challenges arising from such conflicts as that in Sudan have exposed the shortcomings in planning for disasters. In addition to the natural disasters, the homeland security issues related to domestic as well as international terrorism have increased the fear factor and have made 'readiness' the principal priority. Humanitarian logistics is a critical element of an effective disaster relief process. The objective of this monograph is to discuss research issues and potential actions surrounding the new field of humanitarian logistics. We define humanitarian logistics as that special branch of logistics which manages response supply chain of critical supplies and services with challenges such as demand surges, uncertain supplies, critical time windows in face of infrastructure vulnerabilities and vast scope and size of operations. We survey case studies to learn from the past experience and review analytical models from the literature to understand the state-of-the-art in humanitarian logistics. We recommend further research in the fields of operations management and operations research to improve the effectiveness and efficiency of humanitarian logistics. We conclude that through humanitarian logistics is inherently chaotic and complex, and it is difficult to do research in this area, the complexity and obstacles can be dealt with by the researchers

Disaster management from a POM perspective : mapping a new domain

We have reviewed disaster management research papers published in major operations management, management science, operations research, supply chain management and transportation/ logistics journals. In reviewing these papers our objective is to assess and present the macro level “architectural blue print” of disaster management research with the hope that it will attract new researchers and motivate established researchers to contribute to this important field. The secondary objective is to bring this disaster research to the attention of disaster administrators so that disasters are managed more efficiently and more effectively. We have mapped the disaster management research on the following five attributes of a disaster: (1) Disaster Management Function (decision making process, prevention and mitigation, evacuation, humanitarian logistics, casualty management, and recovery and restoration), (2) Time of Disaster (before, during and after), (3) Type of Disaster (accidents, earthquakes, floods, hurricanes, landslides, terrorism and wildfires etc.), (4) Data Type (Field and Archival data, Real data and Hypothetical data), and (5) Data Analysis Technique (bidding models, decision analysis, expert systems, fuzzy system analysis, game theory, heuristics, mathematical programming, network flow models, queuing theory, simulation and statistical analysis). We have done cross tabulations of data among these five parameters to gain greater insights in disaster research. Recommendations for future research are provided